Aleph General Intelligence Convergence Engine (AGICE) Reasoning Model - Evidence-Native, Policy-Governed Reasoning with Rollback and Geometrical Multi-Dimensional Transformation (GMDT) via the Complex Operator (−i)

Aleph General Intelligence Convergence Engine — Preprint (Pending arXiv publishing) Patent Pending. This work is the subject of a pending patent filing: U.S. Provisional Patent Application No. 63/978,753. The materials in this repository/record are provided for research and verification purposes. This Zenodo record provides a timestamped copy of the manuscript titled “Aleph General Intelligence Convergence Engine (AGICE)”. The companion reproducibility artifacts (evidence bundles, offline verification, and deterministic replay tooling) are released separately as the AGICE Evidence Pack: DOI: [https://doi.org/10.5281/zenodo.18720648]. What AGICE targets (operational, testable) AGICE is designed for unknown-problem mode: solving tasks using only (Task Specification, Verifier Pack), without task-specific fine-tuning and without a bespoke handcrafted solver per task family. The core capability claim is operational: Executive governance: policy-governed draft→verify→commit, bounded incremental progress (contract), rollback capability, deterministic arbitration with recorded rationales. Counterfactual projection / GMDT: when repair becomes anti-aligned with verifier improvement, generate structured alternatives via labeled projections (e.g., base, minus_i, and multi-axis extensions) and select strictly via verifiers/arbitration (not temperature noise). Evidence-native auditability: emit replayable, integrity-checkable evidence bundles (hash-chained trajectories + manifests + offline verification) so claims can be independently inspected. Two operational categories toward generalizable intelligenceWe use “general intelligence” in an operational sense: mechanisms that generalize across tasks via governed search and counterfactual learning from failure, backed by audit-grade evidence. We do not claim “human parity.” We claim two building blocks that resemble indispensable properties of generalizable reasoning systems. Category 1 — Governance, Control, and Policy (Mission-Critical Reasoning) What AGICE implements. AGICE implements an executive controller that: holds a goal and a policy across rounds, enforces bounded, testable progress under an incremental contract, verifies intermediate artifacts before commitment, supports rollback and deterministic selection with recorded rationale, and emits evidence explaining why each decision was taken. Why it matters for general intelligence (analogy + mechanism). Human executive function keeps objectives stable, suppresses irrelevant thoughts, and revises plans when contradictions appear. Current LLM/RM generation is frequently feed-forward: once text is produced, downstream steps build on it even if it was wrong. AGICE operationalizes a step toward generalizable intelligence by converting “thought generation” into a controlled decision process:• draft vs commit: proposals are sandboxed, verified, then committed;• rollback: contradictions trigger reversible course correction;• action → consequence: verifiers turn actions into explicit outcome signals;• interpretability-by-construction: selection rationales are logged as first-class outputs. Category 2 — Multi-perspective analysis and meaningful projection (GMDT/ MA−i) What AGICE implements. When iterative repair becomes anti-aligned with verifier improvement, AGICE does not merely retry stochastically. It branches into labeled projections (e.g., base, minus_i), generated via a geometrical transformation of the candidate/diagnostic state using GMDT and the complex operator (−i), then verifies and selects deterministically. Why it matters for general intelligence (analogy + mechanism). Humans do counterfactual learning: they analyze failure through lenses (intent/method/environment), locate the fork where reasoning went wrong, and try a targeted alternative. In AI terms, this is the difference between:• random flow: “try again” because temperature changed, vs.• meaningful projection: “try the structured inverse of the mistake direction.” AGICE’s Category 2 operationalizes this as a verifier-grounded inversion step: failure istransformed into a structured directional hypothesis (projection label + axis), narrowing search andproducing auditable alternatives.